Proactive roaming handshakes based on mobility graphs

ABSTRACT

In one embodiment, a service maintains a mobility path graph that represents roaming transitions between wireless access points in a network by one or more client devices in the network. The service identifies, using the mobility path graph, one of the wireless access points in the network to which a particular client device is predicted to roam. The service performs, in advance of the particular client device initiating roaming to the one or more wireless access points, one or more roaming handshakes on behalf of the particular client device and with respect to the wireless access point to which the particular client device is predicted to roam. The service sends handshake data from the performed one or more roaming handshakes to the identified access point to which the particular client device is predicted to roam.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, proactive roaming handshakes in wireless networks based onmobility graphs.

BACKGROUND

In most wireless networks, such as Wi-Fi networks, roaming is a fairlycommon event. Generally, roaming refers to a client device transitioningfrom one wireless access point (AP) to another. Notably, roaming isoften caused by the client device attempting to connect to the “best” APavailable in the location of the client. The “best” AP from thestandpoint of the client device may change over time due to movement ofthe client, changes in the environment that affect the signal (e.g., interms of strength, signal to noise ratio, etc.), or other such factors.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example communication network;

FIG. 2 illustrates an example network device/node;

FIG. 3 illustrates an example network assurance system;

FIG. 4 illustrates a plot of example roaming handshake delays;

FIG. 5 illustrates an example architecture for performing proactiveroaming handshakes;

FIGS. 6A-6E illustrate examples of mobility paths in a network;

FIGS. 7A-7B illustrate examples of using a path mobility graph to assessmetrics regarding roaming delays;

FIG. 8 illustrates an example diagram of the performance of proactivehandshakes in a wireless network; and

FIG. 9 illustrates an example simplified procedure for performing aproactive roaming handshake based on a mobility graph.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a servicemaintains a mobility path graph that represents roaming transitionsbetween wireless access points in a network by one or more clientdevices in the network. The service identifies, using the mobility pathgraph, one of the wireless access points in the network to which aparticular client device is predicted to roam. The service performs, inadvance of the particular client device initiating roaming to the one ormore wireless access points, one or more roaming handshakes on behalf ofthe particular client device and with respect to the wireless accesspoint to which the particular client device is predicted to roam. Theservice sends handshake data from the performed one or more roaminghandshakes to the identified access point to which the particular clientdevice is predicted to roam.

DESCRIPTION

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,with the types ranging from local area networks (LANs) to wide areanetworks (WANs). LANs typically connect the nodes over dedicated privatecommunications links located in the same general physical location, suchas a building or campus. WANs, on the other hand, typically connectgeographically dispersed nodes over long-distance communications links,such as common carrier telephone lines, optical lightpaths, synchronousoptical networks (SONET), or synchronous digital hierarchy (SDH) links,or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, andothers. The Internet is an example of a WAN that connects disparatenetworks throughout the world, providing global communication betweennodes on various networks. The nodes typically communicate over thenetwork by exchanging discrete frames or packets of data according topredefined protocols, such as the Transmission Control Protocol/InternetProtocol (TCP/IP). In this context, a protocol consists of a set ofrules defining how the nodes interact with each other. Computer networksmay be further interconnected by an intermediate network node, such as arouter, to extend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or PLC networks. That is, in addition to one or more sensors,each sensor device (node) in a sensor network may generally be equippedwith a radio transceiver or other communication port such as PLC, amicrocontroller, and an energy source, such as a battery. Often, smartobject networks are considered field area networks (FANs), neighborhoodarea networks (NANs), personal area networks (PANs), etc. Generally,size and cost constraints on smart object nodes (e.g., sensors) resultin corresponding constraints on resources such as energy, memory,computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics. For the sake ofillustration, a given customer site may fall under any of the followingcategories:

1.) Site Type A: a site connected to the network (e.g., via a private orVPN link) using a single CE router and a single link, with potentially abackup link (e.g., a 3G/4G/LTE backup connection). For example, aparticular CE router 110 shown in network 100 may support a givencustomer site, potentially also with a backup link, such as a wirelessconnection.

2.) Site Type B: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection). A site of type B may itselfbe of different types:

2a.) Site Type B1: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection).

2b.) Site Type B2: a site connected to the network using one MPLS VPNlink and one link connected to the public Internet, with potentially abackup link (e.g., a 3G/4G/LTE connection). For example, a particularcustomer site may be connected to network 100 via PE-3 and via aseparate Internet connection, potentially also with a wireless backuplink.

2c.) Site Type B3: a site connected to the network using two linksconnected to the public Internet, with potentially a backup link (e.g.,a 3G/4G/LTE connection).

Notably, MPLS VPN links are usually tied to a committed service levelagreement, whereas Internet links may either have no service levelagreement at all or a loose service level agreement (e.g., a “GoldPackage” Internet service connection that guarantees a certain level ofperformance to a customer site).

3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but withmore than one CE router (e.g., a first CE router connected to one linkwhile a second CE router is connected to the other link), andpotentially a backup link (e.g., a wireless 3G/4G/LTE backup link). Forexample, a particular customer site may include a first CE router 110connected to PE-2 and a second CE router 110 connected to PE-3.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations.

Servers 152-154 may include, in various embodiments, a networkmanagement server (NMS), a dynamic host configuration protocol (DHCP)server, a constrained application protocol (CoAP) server, an outagemanagement system (OMS), an application policy infrastructure controller(APIC), an application server, etc. As would be appreciated, network 100may include any number of local networks, data centers, cloudenvironments, devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc.

In various embodiments, network 100 may include one or more meshnetworks, such as an Internet of Things network. Loosely, the term“Internet of Things” or “IoT” refers to uniquely identifiable objects(things) and their virtual representations in a network-basedarchitecture. In particular, the next frontier in the evolution of theInternet is the ability to connect more than just computers andcommunications devices, but rather the ability to connect “objects” ingeneral, such as lights, appliances, vehicles, heating, ventilating, andair-conditioning (HVAC), windows and window shades and blinds, doors,locks, etc. The “Internet of Things” thus generally refers to theinterconnection of objects (e.g., smart objects), such as sensors andactuators, over a computer network (e.g., via IP), which may be thepublic Internet or a private network.

Notably, shared-media mesh networks, such as wireless or PLC networks,etc., are often on what is referred to as Low-Power and Lossy Networks(LLNs), which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

In contrast to traditional networks, LLNs face a number of communicationchallenges. First, LLNs communicate over a physical medium that isstrongly affected by environmental conditions that change over time.Some examples include temporal changes in interference (e.g., otherwireless networks or electrical appliances), physical obstructions(e.g., doors opening/closing, seasonal changes such as the foliagedensity of trees, etc.), and propagation characteristics of the physicalmedia (e.g., temperature or humidity changes, etc.). The time scales ofsuch temporal changes can range between milliseconds (e.g.,transmissions from other transceivers) to months (e.g., seasonal changesof an outdoor environment). In addition, LLN devices typically uselow-cost and low-power designs that limit the capabilities of theirtransceivers. In particular, LLN transceivers typically provide lowthroughput. Furthermore, LLN transceivers typically support limited linkmargin, making the effects of interference and environmental changesvisible to link and network protocols. The high number of nodes in LLNsin comparison to traditional networks also makes routing, quality ofservice (QoS), security, network management, and traffic engineeringextremely challenging, to mention a few.

FIG. 2 is a schematic block diagram of an example node/device 200 thatmay be used with one or more embodiments described herein, e.g., as anyof the computing devices shown in FIGS. 1A-1B, particularly the PErouters 120, CE routers 110, nodes/device 10-20, servers 152-154 (e.g.,a network controller located in a data center, etc.), any othercomputing device that supports the operations of network 100 (e.g.,switches, etc.), or any of the other devices referenced below. Thedevice 200 may also be any other suitable type of device depending uponthe type of network architecture in place, such as IoT nodes, etc.Device 200 comprises one or more network interfaces 210, one or moreprocessors 220, and a memory 240 interconnected by a system bus 250, andis powered by a power supply 260.

The network interfaces 210 include the mechanical, electrical, andsignaling circuitry for communicating data over physical links coupledto the network 100. The network interfaces may be configured to transmitand/or receive data using a variety of different communicationprotocols. Notably, a physical network interface 210 may also be used toimplement one or more virtual network interfaces, such as for virtualprivate network (VPN) access, known to those skilled in the art.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor(s) 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise necessaryelements or logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242 (e.g., theInternetworking Operating System, or IOS®, of Cisco Systems, Inc.,another operating system, etc.), portions of which are typicallyresident in memory 240 and executed by the processor(s), functionallyorganizes the node by, inter alia, invoking network operations insupport of software processors and/or services executing on the device.These software processors and/or services may comprise a networkassurance process 248, as described herein, any of which mayalternatively be located within individual network interfaces.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while processes may be shown and/or describedseparately, those skilled in the art will appreciate that processes maybe routines or modules within other processes.

Network assurance process 248 includes computer executable instructionsthat, when executed by processor(s) 220, cause device 200 to performnetwork assurance functions as part of a network assuranceinfrastructure within the network. In general, network assurance refersto the branch of networking concerned with ensuring that the networkprovides an acceptable level of quality in terms of the user experience.For example, in the case of a user participating in a videoconference,the infrastructure may enforce one or more network policies regardingthe videoconference traffic, as well as monitor the state of thenetwork, to ensure that the user does not perceive potential issues inthe network (e.g., the video seen by the user freezes, the audio outputdrops, etc.).

In some embodiments, network assurance process 248 may use any number ofpredefined health status rules, to enforce policies and to monitor thehealth of the network, in view of the observed conditions of thenetwork. For example, one rule may be related to maintaining the serviceusage peak on a weekly and/or daily basis and specify that if themonitored usage variable exceeds more than 10% of the per day peak fromthe current week AND more than 10% of the last four weekly peaks, aninsight alert should be triggered and sent to a user interface.

Another example of a health status rule may involve client transitionevents in a wireless network. In such cases, whenever there is a failurein any of the transition events, the wireless controller may send areason_code to the assurance system. To evaluate a rule regarding theseconditions, the network assurance system may then group 150 failuresinto different “buckets” (e.g., Association, Authentication, Mobility,DHCP, WebAuth, Configuration, Infra, Delete, De-Authorization) andcontinue to increment these counters per service set identifier (SSID),while performing averaging every five minutes and hourly. The system mayalso maintain a client association request count per SSID every fiveminutes and hourly, as well. To trigger the rule, the system mayevaluate whether the error count in any bucket has exceeded 20% of thetotal client association request count for one hour.

In various embodiments, network assurance process 248 may also utilizemachine learning techniques, to enforce policies and to monitor thehealth of the network. In general, machine learning is concerned withthe design and the development of techniques that take as inputempirical data (such as network statistics and performance indicators),and recognize complex patterns in these data. One very common patternamong machine learning techniques is the use of an underlying model M,whose parameters are optimized for minimizing the cost functionassociated to M, given the input data. For instance, in the context ofclassification, the model M may be a straight line that separates thedata into two classes (e.g., labels) such that M=a*x+b*y+c and the costfunction would be the number of misclassified points. The learningprocess then operates by adjusting the parameters a,b,c such that thenumber of misclassified points is minimal. After this optimization phase(or learning phase), the model M can be used very easily to classify newdata points. Often, M is a statistical model, and the cost function isinversely proportional to the likelihood of M, given the input data.

In various embodiments, network assurance process 248 may employ one ormore supervised, unsupervised, or semi-supervised machine learningmodels. Generally, supervised learning entails the use of a training setof data, as noted above, that is used to train the model to apply labelsto the input data. For example, the training data may include samplenetwork observations that do, or do not, violate a given network healthstatus rule and are labeled as such. On the other end of the spectrumare unsupervised techniques that do not require a training set oflabels. Notably, while a supervised learning model may look forpreviously seen patterns that have been labeled as such, an unsupervisedmodel may instead look to whether there are sudden changes in thebehavior. Semi-supervised learning models take a middle ground approachthat uses a greatly reduced set of labeled training data.

Example machine learning techniques that network assurance process 248can employ may include, but are not limited to, nearest neighbor (NN)techniques (e.g., k-NN models, replicator NN models, etc.), statisticaltechniques (e.g., Bayesian networks, etc.), clustering techniques (e.g.,k-means, mean-shift, etc.), neural networks (e.g., reservoir networks,artificial neural networks, etc.), support vector machines (SVMs),logistic or other regression, Markov models or chains, principalcomponent analysis (PCA) (e.g., for linear models), multi-layerperceptron (MLP) ANNs (e.g., for non-linear models), replicatingreservoir networks (e.g., for non-linear models, typically for timeseries), random forest classification, or the like.

The performance of a machine learning model can be evaluated in a numberof ways based on the number of true positives, false positives, truenegatives, and/or false negatives of the model. For example, the falsepositives of the model may refer to the number of times the modelincorrectly predicted whether a network health status rule was violated.Conversely, the false negatives of the model may refer to the number oftimes the model predicted that a health status rule was not violatedwhen, in fact, the rule was violated. True negatives and positives mayrefer to the number of times the model correctly predicted whether arule was violated or not violated, respectively. Related to thesemeasurements are the concepts of recall and precision. Generally, recallrefers to the ratio of true positives to the sum of true positives andfalse negatives, which quantifies the sensitivity of the model.Similarly, precision refers to the ratio of true positives the sum oftrue and false positives.

FIG. 3 illustrates an example network assurance system 300, according tovarious embodiments. As shown, at the core of network assurance system300 may be a cloud service 302 that leverages machine learning insupport of cognitive analytics for the network, predictive analytics(e.g., models used to predict user experience, etc.), troubleshootingwith root cause analysis, and/or trending analysis for capacityplanning. Generally, architecture 300 may support both wireless andwired network, as well as LLNs/IoT networks.

In various embodiments, cloud service 302 may oversee the operations ofthe network of an entity (e.g., a company, school, etc.) that includesany number of local networks. For example, cloud service 302 may overseethe operations of the local networks of any number of branch offices(e.g., branch office 306) and/or campuses (e.g., campus 308) that may beassociated with the entity. Data collection from the various localnetworks/locations may be performed by a network data collectionplatform 304 that communicates with both cloud service 302 and themonitored network of the entity.

The network of branch office 306 may include any number of wirelessaccess points 320 (e.g., a first access point AP1 through nth accesspoint, APn) through which endpoint nodes may connect. Access points 320may, in turn, be in communication with any number of wireless LANcontrollers (WLCs) 326 (e.g., supervisory devices that provide controlover APs) located in a centralized datacenter 324. For example, accesspoints 320 may communicate with WLCs 326 via a VPN 322 and network datacollection platform 304 may, in turn, communicate with the devices indatacenter 324 to retrieve the corresponding network feature data fromaccess points 320, WLCs 326, etc. In such a centralized model, accesspoints 320 may be flexible access points and WLCs 326 may be N+1 highavailability (HA) WLCs, by way of example.

Conversely, the local network of campus 308 may instead use any numberof access points 328 (e.g., a first access point AP1 through nth accesspoint APm) that provide connectivity to endpoint nodes, in adecentralized manner. Notably, instead of maintaining a centralizeddatacenter, access points 328 may instead be connected to distributedWLCs 330 and switches/routers 332. For example, WLCs 330 may be 1:1 HAWLCs and access points 328 may be local mode access points, in someimplementations.

To support the operations of the network, there may be any number ofnetwork services and control plane functions 310. For example, functions310 may include routing topology and network metric collection functionssuch as, but not limited to, routing protocol exchanges, pathcomputations, monitoring services (e.g., NetFlow or IPFIX exporters),etc. Further examples of functions 310 may include authenticationfunctions, such as by an Identity Services Engine (ISE) or the like,mobility functions such as by a Connected Mobile Experiences (CMX)function or the like, management functions, and/or automation andcontrol functions such as by an APIC-Enterprise Manager (APIC-EM).

During operation, network data collection platform 304 may receive avariety of data feeds that convey collected data 334 from the devices ofbranch office 306 and campus 308, as well as from network services andnetwork control plane functions 310. Example data feeds may comprise,but are not limited to, management information bases (MIBS) with SimpleNetwork Management Protocol (SNMP) v2, JavaScript Object Notation (JSON)Files (e.g., WSA wireless, etc.), NetFlow/IPFIX records, logs reportingin order to collect rich datasets related to network control planes(e.g., Wi-Fi roaming, join and authentication, routing, QoS, PHY/MACcounters, links/node failures), traffic characteristics, and other suchtelemetry data regarding the monitored network. As would be appreciated,network data collection platform 304 may receive collected data 334 on apush and/or pull basis, as desired. Network data collection platform 304may prepare and store the collected data 334 for processing by cloudservice 302. In some cases, network data collection platform may alsoanonymize collected data 334 before providing the anonymized data 336 tocloud service 302.

In some cases, cloud service 302 may include a data mapper andnormalizer 314 that receives the collected and/or anonymized data 336from network data collection platform 304. In turn, data mapper andnormalizer 314 may map and normalize the received data into a unifieddata model for further processing by cloud service 302. For example,data mapper and normalizer 314 may extract certain data features fromdata 336 for input and analysis by cloud service 302.

In various embodiments, cloud service 302 may include a machinelearning-based analyzer 312 configured to analyze the mapped andnormalized data from data mapper and normalizer 314. Generally, analyzer312 may comprise a power machine learning-based engine that is able tounderstand the dynamics of the monitored network, as well as to predictbehaviors and user experiences, thereby allowing cloud service 302 toidentify and remediate potential network issues before they happen.

Machine learning-based analyzer 312 may include any number of machinelearning models to perform the techniques herein, such as for cognitiveanalytics, predictive analysis, and/or trending analytics as follows:

-   -   Cognitive Analytics Model(s): The aim of cognitive analytics is        to find behavioral patterns in complex and unstructured        datasets. For the sake of illustration, analyzer 312 may be able        to extract patterns of Wi-Fi roaming in the network and roaming        behaviors (e.g., the “stickiness” of clients to APs 320, 328,        “ping-pong” clients, the number of visited APs 320, 328, roaming        triggers, etc). Analyzer 312 may characterize such patterns by        the nature of the device (e.g., device type, OS) according to        the place in the network, time of day, routing topology, type of        AP/WLC, etc., and potentially correlated with other network        metrics (e.g., application, QoS, etc.). In another example, the        cognitive analytics model(s) may be configured to extract AP/WLC        related patterns such as the number of clients, traffic        throughput as a function of time, number of roaming processed,        or the like, or even end-device related patterns (e.g., roaming        patterns of iPhones, IoT Healthcare devices, etc.).    -   Predictive Analytics Model(s): These model(s) may be configured        to predict user experiences, which is a significant paradigm        shift from reactive approaches to network health. For example,        in a Wi-Fi network, analyzer 312 may be configured to build        predictive models for the joining/roaming time by taking into        account a large plurality of parameters/observations (e.g., RF        variables, time of day, number of clients, traffic load,        DHCP/DNS/Radius time, AP/WLC loads, etc.). From this, analyzer        312 can detect potential network issues before they happen.        Furthermore, should abnormal joining time be predicted by        analyzer 312, cloud service 312 will be able to identify the        major root cause of this predicted condition, thus allowing        cloud service 302 to remedy the situation before it occurs. The        predictive analytics model(s) of analyzer 312 may also be able        to predict other metrics such as the expected throughput for a        client using a specific application. In yet another example, the        predictive analytics model(s) may predict the user experience        for voice/video quality using network variables (e.g., a        predicted user rating of 1-5 stars for a given session, etc.),        as function of the network state. As would be appreciated, this        approach may be far superior to traditional approaches that rely        on a mean opinion score (MOS). In contrast, cloud service 302        may use the predicted user experiences from analyzer 312 to        provide information to a network administrator or architect in        real-time and enable closed loop control over the network by        cloud service 302, accordingly. For example, cloud service 302        may signal to a particular type of endpoint node in branch        office 306 or campus 308 (e.g., an iPhone, an IoT healthcare        device, etc.) that better QoS will be achieved if the device        switches to a different AP 320 or 328.    -   Trending Analytics Model(s): The trending analytics model(s) may        include multivariate models that can predict future states of        the network, thus separating noise from actual network trends.        Such predictions can be used, for example, for purposes of        capacity planning and other “what-if” scenarios.

Machine learning-based analyzer 312 may be specifically tailored for usecases in which machine learning is the only viable approach due to thehigh dimensionality of the dataset and patterns cannot otherwise beunderstood and learned. For example, finding a pattern so as to predictthe actual user experience of a video call, while taking into accountthe nature of the application, video CODEC parameters, the states of thenetwork (e.g., data rate, RF, etc.), the current observed load on thenetwork, destination being reached, etc., is simply impossible usingpredefined rules in a rule-based system.

Unfortunately, there is no one-size-fits-all machine learningmethodology that is capable of solving all, or even most, use cases. Inthe field of machine learning, this is referred to as the “No FreeLunch” theorem. Accordingly, analyzer 312 may rely on a set of machinelearning processes that work in conjunction with one another and, whenassembled, operate as a multi-layered kernel. This allows networkassurance system 300 to operate in real-time and constantly learn andadapt to new network conditions and traffic characteristics. In otherwords, not only can system 300 compute complex patterns in highlydimensional spaces for prediction or behavioral analysis, but system 300may constantly evolve according to the captured data/observations fromthe network.

Cloud service 302 may also include output and visualization interface318 configured to provide sensory data to a network administrator orother user via one or more user interface devices (e.g., an electronicdisplay, a keypad, a speaker, etc.). For example, interface 318 maypresent data indicative of the state of the monitored network, currentor predicted issues in the network (e.g., the violation of a definedrule, etc.), insights or suggestions regarding a given condition orissue in the network, etc. Cloud service 302 may also receive inputparameters from the user via interface 318 that control the operation ofsystem 300 and/or the monitored network itself. For example, interface318 may receive an instruction or other indication to adjust/retrain oneof the models of analyzer 312 from interface 318 (e.g., the user deemsan alert/rule violation as a false positive).

In various embodiments, cloud service 302 may further include anautomation and feedback controller 316 that provides closed-loop controlinstructions 338 back to the various devices in the monitored network.For example, based on the predictions by analyzer 312, the evaluation ofany predefined health status rules by cloud service 302, and/or inputfrom an administrator or other user via input 318, controller 316 mayinstruct an endpoint client device, networking device in branch office306 or campus 308, or a network service or control plane function 310,to adjust its operations (e.g., by signaling an endpoint to use aparticular AP 320 or 328, etc.).

As noted above, in a wireless network, such as a Wi-Fi network, when aclient moves from one Access Point (AP) to another, roaming events aretriggered. These roaming events are potentially costly in terms ofcontrol plane operations and are also prone to failure, with roamingfailure rates of 20% or more being common in many networks. Based on theAPs involved in the roaming, the type of roaming may be classified asbeing Layer-2 roaming, Layer-3 roaming, intra-WLC roaming, etc. Thehandshake and the amount of time required for the roaming operationgenerally depends on the type of roaming involved.

FIG. 4 illustrates a plot 400 of example roaming handshake delays.Typically, successful roaming handshakes take anywhere between a fewseconds to tens of seconds to complete. As shown, this time can varygreatly, depending on the type of roaming involved. For example,intra-WLC roaming (denoted IntraRoam) is a relatively quick operation,as the roaming occurs between two APs connected to the same WLC. In sucha case, only authentication is needed to complete the handshake. Layer-2(L2) roaming is a somewhat slower operation and entails the clientroaming between APs on the same client subnet and requiring onlyauthentication during the handshake. Finally, Fast Roaming events (e.g.,Layer 3 roaming) may entail a handshake that has authentication, DHCP,and other steps, leading to a much longer handshake delay than that ofthe other roaming types.

More specifically, during a roaming event in which a client device movesfrom wireless AP to another (e.g., between Wi-Fi APs, etc.), roaminghandshakes may be performed for any or all of the following reasons:

-   -   Association—to associate the client with a particular AP.    -   Authentication—to authenticate the client and its access to the        wireless network.    -   Mobility—to indicate when the client moves from one WLC to        another.    -   DHCP—to configure the client, such as with a particular IP        address.

Non-elastic applications, such as voice and video calls, suffer heavilywhen the client device is roaming, leading to unacceptable disruptions.Thus, the roaming delays experienced by the user are strongly dependenton the amount of time needed to complete the roaming handshakes.

Proactive Roaming Handshakes Based on Mobility Graphs

The techniques introduced herein allow for proactive roaming handshakesto be made using mobility graphs, thereby allowing for faster clientroaming in a wireless network. In some aspects, the techniques hereinintroduce the concept of computing roaming delays over client/userpaths, which provides information about the degradation of userexperience at different locations due to the delay in link setup timeswhile roaming. In another aspect, the techniques herein also introduce aproactive engine that uses the roaming delay information and usermobility pattern to proactively keep the roaming related informationready when the user moves the client to a new area. This proactiveengine, in some embodiments, may leverage machine learning, to predictwhen handshakes should occur, based on mobility predictions for theclient device. In a further aspect, the techniques herein proactivelyinitiate local reroutes or AP association changes if the system predictsthat the user will have roaming failures. In other words, such aproactive scheme will predict the possible failure and mitigate failuresbefore they occur. As would be appreciated, while the techniques hereinare described primarily with respect to Wi-Fi networks, the techniquesare not limited as such and could be used in the broader context ofdevice mobility (e.g., in 4G/5G/LTE networks, IoT and LLN networks thatuse 802.15.4, etc.).

Specifically, according to one or more embodiments of the disclosure asdescribed in detail below, a service maintains a mobility path graphthat represents roaming transitions between wireless access points in anetwork by one or more client devices in the network. The serviceidentifies, using the mobility path graph, one of the wireless accesspoints in the network to which a particular client device is predictedto roam. The service performs, in advance of the particular clientdevice initiating roaming to the one or more wireless access points, oneor more roaming handshakes on behalf of the particular client device andwith respect to the wireless access point to which the particular clientdevice is predicted to roam. The service sends handshake data from theperformed one or more roaming handshakes to the identified access pointto which the particular client device is predicted to roam.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with thenetwork assurance process 248, which may include computer executableinstructions executed by the processor 220 (or independent processor ofinterfaces 210) to perform functions relating to the techniquesdescribed herein.

Operationally, FIG. 5 illustrates an example architecture 500 forperforming proactive roaming handshakes in a wireless network based onmobility graphs, according to various embodiments. At the core ofarchitecture 500 may be the following components: a roaming delayanalyzer 506, a proactive handshake initiator 508, and/or a proactivereroute trigger 510. In some implementations, the components ofarchitecture 500 may be implemented within a network assurance system,such as system 300 shown in FIG. 3. Accordingly, the components 506-510of architecture 500 shown may be implemented as part of cloud service302, as part of network data collection platform 304, and/or on one ormore network elements/entities within the monitored network itself. Forexample, components 506-510 may be implemented as part of machinelearning-based analyzer 312, in some embodiments, as shown. Further,these components may be implemented in a distributed manner orimplemented as its own stand-alone service, either as part of the localnetwork under observation or as a remote service. In addition, thefunctionalities of the components of architecture 500 may be combined,omitted, or implemented as part of other processes, as desired.

During operation, a client device 504 may leverage one or more ofnetwork entities 502, to communicate wirelessly with the local network.For example, network entities 502 may include wireless APs, WLC,switches, routers, or the like, that provide network connectivity toclient device 504. In turn, network entities 502 may report informationregarding the roaming and other wireless conditions associated withclient device 504 to network data collection platform 304 as part ofdata 334. Network data collection platform 304 may then pass this dataon to cloud service 302 for analysis by ML-based analyzer 312.

Roaming delay analyzer 506 may be configured to compute and storeroaming metrics for different client mobility paths observed in thenetwork, according to various embodiments. In contrast with a data planepath, a mobility path generally refers to the list of APs that a clientdevice visits/attaches to in a given period. As would be appreciated, anaccess point may be a Wi-Fi AP, a gateway in the context of the IoT, abase station, or any other networking device that communicateswirelessly with a client device and provides the client device access tothe wireless network. In addition, in various embodiments, roaming delayanalyzer 502 may be located in cloud service 302 but, alternatively, maybe implemented as a service at the WLC or AP level, as well.

FIGS. 6A-6E illustrate examples of a mobility path in a network,according to various embodiments. In some cases, a given mobility pathmay be defined as an ordered set of three or more AP nodes. In otherwords, a mobility path is a control plane path that is not followed bythe data, but by the client device itself (e.g., as a result of physicalmovement of the client device from AP to AP).

In FIG. 6A, assume that a client device 602 is a mobile device that istraveling along a path of travel 606. As would be appreciated, whilepath of travel 606 is depicted as a linear path, the movement of amobile device in most situations will not be linear and may vary in one,two, or even three dimensions. For purposes of illustration, assume thatthe local network comprises APs 604, such as APs 604 a-604 c (e.g., APsA-C), as shown. At time T=t₀, client device 602 may be connected to thewireless network via AP 604 a, which may be the closest AP 604 to clientdevice 602 at this time or, alternatively, offer the bestcharacteristics in terms of signal strength, SNR, etc.

In FIG. 6B, assume now that client device 602 has moved along path oftravel 606 and is now closer to AP 604 b at time T=t₁. If the wirelesscharacteristics of AP 604 b, from the standpoint of client device 602,are now better than that of AP 604 a, client device 602 may initiateroaming. As a result, client device 602 may attach itself to AP 604 band detach itself from AP 604 a, thereby completing the roamingoperation. After attaching to AP 604 b, client device 602 may continueto communicate with the network as normal via AP 604 b.

In FIG. 6C, client device 602 may perform a similar operation as in FIG.6C, but with AP 604 c. Notably, assume now that at time T=t₂, clientdevice 602 is now within closest proximity to AP 604 c and/or that AP604 c offers the best characteristics, from the perspective of clientdevice 602. In such a case, client device 602 may initiate anotherroaming operation, thereby switching its access from AP 604 b to AP 604c.

FIG. 6D illustrates the concept of a mobility path 608, according tovarious embodiments. Based on the movement and wireless roamingoperations of client device 602 over time (e.g., between times T=t₀ andT=t₂), as depicted in FIG. 6A-6C, client device 602 can be viewed ashaving traversed mobility path 608. More specifically, mobility path 608may be a directed (or directionless) set of nodes/APs 604 through whichthe client device 602 roamed in the network. In this sense, roamingevents between the APs 604 can be viewed akin to hops between nodes in acommunication data path.

Mobility paths can also be represented in three dimensions, as shown inFIG. 6E. Notably, plot 610 illustrates in three dimensions the mobilitypaths 612 a-612 e observed during testing of a wireless network. In somecases, such as with mobility path 612 b and 612 d, the client devicesmay tend to stay at the same z-coordinate, indicating that the clientdevice is likely to roam along a mobility path on a single floor.However, such as in the case of mobility path 612 e, the client devicemay roam between APs with different z-coordinates, indicating that theuser of the device may have traveled to a different floor.

Referring again to FIG. 5, roaming delay analyzer 506 may use theroaming data collected from the monitored network, to construct aroaming delay graph for the wireless network. In one embodiment, roamingdelay analyzer 506 may use machine learning to first compute the mosttraveled mobility paths in the network and construct a mobility pathgraph (e.g., a model of the movement of the client device(s) in thenetwork). A node in the constructed graph may represent an AP with edgesof the graph representing roaming events from one AP to another. Inturn, roaming delay analyzer 506 may associate the collected metricsregarding roaming delays with the represented mobility paths in thegraph (e.g., by associating the metrics with the edges of the graph),thereby forming a roaming delay graph for the wireless network. Invarious embodiments, roaming delay analyzer 506 may generate a mobilitypath graph per user, per client device, or as an aggregate of captureddata regarding any number users or client devices.

For each edge of a mobility path graph, roaming delay analyzer 506 mayassociate any or all of the following metrics: type of roaming, time fordifferent stages of the roaming event (e.g., authentication, DHCP,etc.), and handshake specific parameters (e.g., Radius Server IP, DHCPServer IP, etc.). This information can be stored based on time-of-day,client type, or even for individual client devices. In other words,roaming delay analyzer 506 may compute the roaming delay metrics fordifferent mobility paths of the user or client. Further, each metric maybe expressed using any number of different statistics (e.g., min, max,median, 90^(th) percentile, an upper bound computed using a statisticalestimate, etc.).

FIGS. 7A-7B illustrate examples of using a path mobility graph to assessmetrics regarding roaming delays, according to various embodiments. Asshown in graph 700 in FIG. 7A, each AP in the network may be representedas a node 702 with edges 704 representing observed roaming eventsbetween APs in the network. In particular, graph 700 was constructedusing actual data from a live network during prototyping of thetechniques herein. In some cases, the size of a node 702 and/or an edge704 may represent the volume of attached client devices or roamingevents observed. From this, inferences can be made from graph 700, suchas that there is a set of central APs through which users typically roamand that roaming events between certain pairs of APs are much moreprevalent than others.

Associated with each edge 704 may be any number of metrics regardingroaming delays. For example, as shown in FIG. 7B, the top k-number ofclient affecting trajectories can be identified from graph 700 based onthe metrics associated with edges 704. Notably, metrics associated withan edge 704 may include the type of roaming (e.g., L2 roaming, etc.),authentication delay metrics (e.g., min, max, percentiles, standarddeviations, etc.), DHCP delay metrics (e.g., min, max, percentiles,standard deviations, etc.), or the like. These metrics can then be usedto optimize the network by assessing changes to the roaming boundariesin the network. For example, to improve the user experience, high useedges that exhibit poor roaming metrics could be converted to useIntra-WLC roaming instead of L2-roaming, drastically reducing theroaming delays between the corresponding APs.

Referring again to FIG. 5, another component of architecture 500 may beproactive handshake initiator 508, in various embodiments. Generally,proactive handshake initiator 508 may perform any or all of threeseparate tasks. First, proactive handshake initiator 508 may use themobility path graph(s) maintained by roaming delay analyzer 506 toidentify one or more APs or WLCs to which a client device is predictedto roam, as well as the time at which the client device is predicted toinitiate the roaming. In some cases, proactive handshake initiator 508may also determine a measure of confidence in these predictions (e.g.,by selecting the AP with the highest confidence measure as the AP theclient device is most likely to visit next).

Second, proactive handshake initiator 508 may predict the roaminghandshake(s) that the client device will perform when the client devicereaches its next predicted AP. For example, proactive handshakeinitiator 508 may determine that the next AP will be a foreign AP, andthe client device will have to anchor to a home AP.

Third, proactive handshake initiator 508 may proactively initiate therespective roaming handshakes needed before the client device cancomplete its roaming to the predicted next AP. For example, proactivehandshake initiator 508 may proactively communicate with a RemoteAuthentication Dial-In User Service (RADIUS) server, to authenticate andauthorize the client device in advance of the client device initiatingroaming to the next AP. Similarly, if an IP address is needed by theclient device as part of the roaming process, proactive handshakeinitiator 508 may initiate a DHCP request with a DHCP server for an IPaddress on behalf of the client device. All these reservations may bemarked as “proactive,” and, hence, may only reserve these resources andkeep this information ready for when the client device moves withinrange of the new AP. In some cases, none of the resulting handshake datamay be transferred to the new AP until the client device actuallyarrives within range of the new AP and initiates roaming.

FIG. 8 illustrates an example diagram of the performance of proactivehandshakes in a wireless network, according to various embodiments. Asshown, assume that a client device attaches to a first AP serviced by aWLC 802 in part by sending a UserID with an association request to WLC802. Alternatively, or in addition thereto, a device identifier may beused in place of the UserID. If the client device is authorized andallowed to attach to the AP serviced by WLC 802, WLC 802 may then send amobility prediction request to a mobility predictor 804 (e.g., cloudservice 302 hosting any or all of components 506-510, localservices/functions of WLC 802, or the like). Such a request may, forexample, include both the UserID, device ID, or other informationregarding the client device, as well as an identifier for WLC 802 as thecurrent WLC to which the client device is associated.

In response to receiving a mobility prediction request from WLC 802,mobility predictor 804 may respond with a mobility prediction for theclient device. Notably, based on the mobility path graph(s) for thenetwork, mobility predictor 804 may predict that the client device islikely to next roam to an AP serviced by WLC 806, as well as a time atwhich client device is predicted to do so.

Several prediction schemes are possible, to predict the next AP and/orWLC to which the client device is likely to roam. In one embodiment,mobility predictor 804 may simply assess the previous movement patternsof the client device or user associated with the client device. Inanother embodiment, the prediction may be based on the movement of anentire population of users or client devices. In a further embodiment,the prediction may be based on only portion of the entire population ofusers or client devices, such as only those that are similar to theparticular user or client device under analysis. For example, thissimilarity can be based on information such as the role of the user inthe organization, similarities between the movement pattern of theclient device and other client devices, or the like.

In various cases, mobility predictor 804 may also consider only the nextpredicted AP hop for the client device or, alternatively, the nextn-number of hops for the client device. In the case of mobilitypredictor 804 predicting multiple hops, mobility predictor 804 may usethe prediction confidence measures to initiate the proactive roaminghandshake(s) for the most likely n-number of next hops for the clientdevice.

Another parameter that mobility predictor 804 may consider is the typeof roaming that the client device is predicted to initiate. For example,assume that there are two probable next hops for the client device, AP1and AP2, with similar confidence measures (e.g., the client device isequally likely to hop to either of the APs). In addition, assume thatthe client device roaming to AP2 would trigger a Layer-3 type ofroaming, which is more costly. In such a case, mobility predictor 804only triggering the proactive handshake on AP1, and potentially triggera second handshake on a third probable next hop, AP3.

In various embodiments, WLC 802 may use the mobility prediction frommobility predictor 804 to proactively initiate the roaming handshake(s)on behalf of the client device and in advance of the client deviceroaming to the AP serviced by WLC 806. For example, WLC 802 may send arequest to WLC 806 to prepare for the arrival of the client device, inadvance of the client device initiating roaming with one of the APscontrolled by WLC 806.

As shown, WLC 806 may perform an authentication handshake by sending anauthentication request to a RADIUS server 808 in the network. Such arequest may identify, for example, the user (e.g., UserID) of the clientdevice, the client device itself, and/or the time at which the clientdevice is predicted to roam to the AP controlled by WLC 806. Inresponse, RADIUS server 808 may authenticate and pre-authorize theclient device to roam to the AP controlled by WLC 806.

In some embodiments, another roaming handshake that WLC 806 may performon behalf of the client device is a DHCP handshake with DHCP server 810.For example, WLC 806 may request an IP address from DHCP server 810 forthe client device, when the client device roams to the AP controlled byWLC 806. Like the authorization request to RADIUS server 808, the DHCPrequest sent to DHCP server 810 may also indicate the time at which theclient device is expected to need the IP address, thereby allowing DHCPserver 810 to reserve IP address assignments for different clientdevices in the future.

Based on the proactive roaming handshake(s) performed by WLC 806, WLC806 is now ready to complete the roaming operation when the clientdevice actually roams to the AP controlled by WLC 806. Thus, when theuser/client device initiates the roaming operation, WLC 806 can quicklycomplete the roaming operation and, in some cases, notify the previousWLC 802 that the roaming operation has completed.

Referring again to architecture 500 in FIG. 5, another function of thereservation scheme enacted by proactive handshake initiator 508 may beto handle prediction failures when the user/client device does notarrive at the predicted AP. Such failures can be handled by cancelingthe reservation. In one embodiment, the AP may reserve the resources,such as IP address from a DHCP server, or the like, for a given timeperiod. This reservation time period can be based on the time at whichthe client device is predicted to initiate roaming to the AP. Forexample, if the user/client device is expected to arrive at AP-X at timet, but fails to arrive even after t+n seconds, then the reservation maybe cancelled at the AP and the DHCP server. In another embodiment, theresources may be annulled if the client device attaches to a differentAP than the predicted AP. In such cases, resources may also betransferred to the new AP (e.g., an IP address already reserved can betransferred from the predicted AP to the actual AP, etc.), therebyreducing the roaming times even when the client device does not arriveat the predicted AP.

In various embodiments, architecture 500 may also include a proactivereroute trigger module 510 configured to proactively reroute the clientdevice to another AP if any of the handshakes have failed or arepredicted to fail. For example, assume that each AP in the network isconstrained to a maximum number of attached client devices or users atany given time. In the client device under analysis is predicted toarrive at AP-X, and is already at or close to the maximum number ofclients, then AP-X may signal the WLC that the client device is expectedto face a roaming failure if the client device joins the predicted AP-X.In turn, proactive reroute trigger module 510 may cause the WLC to senda notification to the client device, thereby causing the client deviceto attempt roaming with a different AP in the network. In anotherexample, assume that proactive handshake initiator 508 fails to reservean IP for a client device on a first DHCP server (e.g., due toexhaustion of the IP pool of the DHCP server). In such a case, proactivereroute trigger module 510 may trigger the sending of a second DHCPrequest to another DHCP server, to reserve an IP address for the clientdevice.

FIG. 9 illustrates an example simplified procedure for performing aproactive roaming handshake based on a mobility graph, in accordancewith one or more embodiments described herein. For example, anon-generic, specifically configured device (e.g., device 200) thatprovides a service in a network may perform procedure 900 by executingstored instructions (e.g., process 248). The procedure 900 may start atstep 905, and continues to step 910, where, as described in greaterdetail above, the service may maintain a mobility path graph thatrepresents roaming transitions between wireless access points in anetwork by one or more client devices in the network. In someembodiments, such a graph may be for a particular user or client device.In other embodiments, the graph may be for an aggregate of users and/orclient devices in the network. In general, a mobility path graph mayrepresent APs as nodes in the graph. Edges between the nodes may then beassigned any number of observed or predicted metrics, such as the volumeof client devices that roam between any two APs, metrics regarding theroaming operations (e.g., roaming type, handshake times, etc.), or thelike.

At step 915, as detailed above, the service may identify, using themobility path graph, one of the wireless access points in the network towhich a particular client device is predicted to roam. For example, ifthe client device is currently attached to AP-X, analysis of themobility path graph by the service may indicate that the client deviceis most likely to roam to another AP in the network, AP-Y. Thus, in someembodiments, the service may use a machine learning-based model topredict a mobility path of the particular client device, based onprevious movements of the particular client device.

At step 920, the service may perform one or more roaming handshakes onbehalf of the particular client device and with respect to the wirelessaccess point to which the particular client device is predicted to roam,as described in greater detail above. In various embodiments, theservice may do so in advance of the client device initiating roamingwith the predicted next AP. In some embodiments, this may entail sendingan authentication data on behalf of the particular client device to aRADIUS server. In further embodiments, this may entail sending a DHCPrequest to a DHCP server on behalf of the particular client device.

At step 925, as detailed above, the service may send handshake data fromthe performed one or more roaming handshakes to the identified accesspoint to which the particular client device is predicted to roam. Forexample, when the client device actually attempts to roam to thepredicted AP, the service may send authorization data, a new IP, or thelike, to the new AP. By proactively performing the handshakesbeforehand, the roaming time experienced by the client device may bereduced considerably. Procedure 900 then ends at step 930.

It should be noted that while certain steps within procedure 900 may beoptional as described above, the steps shown in FIG. 9 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, provide for a reduced amountof time needed for a client device to roam between APs in a wirelessnetwork. By proactively performing the roaming handshake(s) before aclient device arrives at a predicted next AP in the network, the roamingoperation can be completed much faster. As detailed herein, such roamingtransitions would otherwise impact the user experience in many clientapplications, such as voice and video conferencing.

While there have been shown and described illustrative embodiments thatprovide for proactive roaming handshakes in a wireless network, it is tobe understood that various other adaptations and modifications may bemade within the spirit and scope of the embodiments herein. For example,while certain embodiments are described herein with respect to usingcertain models for purposes of predicting client device movement androaming transitions between APs, the models are not limited as such andmay be used for other functions, in other embodiments. In addition,while certain protocols are shown, such as DHCP, other suitableprotocols may be used, accordingly.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly, this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

What is claimed is:
 1. A method comprising: maintaining, by a service, a mobility path graph that represents roaming transitions between wireless access points in a network by one or more client devices in the network; identifying, by the service and using the mobility path graph, one of the wireless access points in the network to which a particular client device is predicted to roam; performing, by the service and in advance of the particular client device initiating roaming to the one or more wireless access points, one or more roaming handshakes on behalf of the particular client device and with respect to the wireless access point to which the particular client device is predicted to roam, wherein performing the one or more roaming handshakes on behalf of the particular client device includes: determining, by the service, that a first Dynamic Host Configuration Protocol (DHCP) handshake performed by the service on behalf of the particular client with a first DHCP server has failed; and performing, by the service and based on the first DHCP handshake failing, a second DHCP handshake with a second DHCP server on behalf of the particular client; and sending, by the service, handshake data from the performed one or more roaming handshakes to the identified access point to which the particular client device is predicted to roam.
 2. The method as in claim 1, wherein identifying the wireless access point to which the particular client is predicted to roam comprises: using, by the service, a machine learning-based model to predict a mobility path of the particular client device, based on previous movements of the particular client device.
 3. The method as in claim 1, wherein identifying the wireless access point to which the particular client is predicted to roam comprises: using, by the service, a machine learning-based model to predict a mobility path of the particular client device, based on previous movements of a plurality of client devices in the network.
 4. The method as in claim 1, wherein performing the one or more roaming handshakes on behalf of the particular client device and with respect to the wireless access point to which the particular client device is predicted to roam comprises: sending, by the service, authentication data on behalf of the particular client device to a Remote Authentication Dial-In User Service (RADIUS) server.
 5. The method as in claim 1, wherein performing the one or more roaming handshakes on behalf of the particular client device and with respect to the wireless access point to which the particular client device is predicted to roam comprises: sending, by the service, a Dynamic Host Configuration Protocol (DHCP) request to a DHCP server on behalf of the particular client device.
 6. The method as in claim 1, further comprising: predicting, by the service, that a handshake performed by the service on behalf of the particular client will fail; and causing, by the service, the particular client device to roam to a different wireless access point, based on the prediction that the handshake will fail.
 7. The method as in claim 6, wherein the handshake is predicted to fail because the wireless access points in the network to which the particular client device is predicted to roam has reached a maximum number of admitted client devices.
 8. The method as in claim 1, wherein the first DHCP handshake failed because a pool Internet Protocol (IP) addresses of the first DHCP server was exhausted.
 9. An apparatus comprising: one or more network interfaces to communicate with a network; a processor coupled to the network interfaces and configured to execute one or more processes; and a memory configured to store a process executable by the processor, the process when executed configured to: maintain a mobility path graph that represents roaming transitions between wireless access points in a network by one or more client devices in the network; identify, using the mobility path graph, one of the wireless access points in the network to which a particular client device is predicted to roam; perform, in advance of the particular client device initiating roaming to the one or more wireless access points, one or more roaming handshakes on behalf of the particular client device and with respect to the wireless access point to which the particular client device is predicted to roam, wherein the apparatus performs the one or more roaming handshakes on behalf of the particular client device by: determining that a first Dynamic Host Configuration Protocol (DHCP) handshake performed by the service on behalf of the particular client with a first DHCP server has failed; and performing, based on the first DHCP handshake failing, a second DHCP handshake with a second DHCP server on behalf of the particular client; and send handshake data from the performed one or more roaming handshakes to the identified access point to which the particular client device is predicted to roam.
 10. The apparatus as in claim 9, wherein the apparatus identifies the wireless access point to which the particular client is predicted to roam by: using a machine learning-based model to predict a mobility path of the particular client device, based on previous movements of the particular client device.
 11. The apparatus as in claim 9, wherein the apparatus identifies the wireless access point to which the particular client is predicted to roam by: using a machine learning-based model to predict a mobility path of the particular client device, based on previous movements of a plurality of client devices in the network.
 12. The apparatus as in claim 9, wherein the apparatus performs the one or more roaming handshakes on behalf of the particular client device and with respect to the wireless access point to which the particular client device is predicted to roam by: sending authentication data on behalf of the particular client device to a Remote Authentication Dial-In User Service (RADIUS) server.
 13. The apparatus as in claim 11, wherein the apparatus performs the one or more roaming handshakes on behalf of the particular client device and with respect to the wireless access point to which the particular client device is predicted to roam by: sending a Dynamic Host Configuration Protocol (DHCP) request to a DHCP server on behalf of the particular client device.
 14. The apparatus as in claim 9, wherein the process when executed is further configured to: predict that a handshake performed by the apparatus on behalf of the particular client will fail; and cause the particular client device to roam to a different wireless access point, based on the prediction that the handshake will fail.
 15. The apparatus as in claim 14, wherein the handshake is predicted to fail because the wireless access points in the network to which the particular client device is predicted to roam has reached a maximum number of admitted client devices.
 16. The apparatus as in claim 9, wherein the first DHCP handshake failed because a pool Internet Protocol (IP) addresses of the first DHCP server was exhausted.
 17. The apparatus as in claim 9, wherein the apparatus comprises a wireless access point or wireless local area network controller (WLC) to which the particular client device is associated.
 18. A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising: maintaining a mobility path graph that represents roaming transitions between wireless access points in a network by one or more client devices in the network; identifying, using the mobility path graph, one of the wireless access points in the network to which a particular client device is predicted to roam; performing, in advance of the particular client device initiating roaming to the one or more wireless access points, one or more roaming handshakes on behalf of the particular client device and with respect to the wireless access point to which the particular client device is predicted to roam wherein performing the one or more roaming handshakes on behalf of the particular client device includes: determining that a first Dynamic Host Configuration Protocol (DHCP) handshake performed by the service on behalf of the particular client with a first DHCP server has failed; and performing, based on the first DHCP handshake failing, a second DHCP handshake with a second DHCP server on behalf of the particular client; and sending handshake data from the performed one or more roaming handshakes to the identified access point to which the particular client device is predicted to roam. 